Cache Poisoning in Gin with Dynamodb

Cache poisoning in a Gin application that uses DynamoDB typically occurs when unvalidated or attacker-controlled data is used to construct cache keys, responses, or DynamoDB query parameters. Because Gin does not inherently sanitize inputs before they influence caching behavior or DynamoDB requests, an attacker can inject malicious or misleading values that cause the application to store incorrect data in the cache or to query DynamoDB in an unsafe manner.

Consider a scenario where a Gin handler builds a DynamoDB query key from a URL parameter without normalization or strict validation:

// Gin handler example with potential cache key poisoning
func getUserHandler(cache *lru.Cache) gin.HandlerFunc {
    return func(c *gin.Context) {
        userID := c.Param("userID")
        cacheKey := "user:" + userID
        if cached, ok := cache.Get(cacheKey); ok {
            c.JSON(200, cached)
            return
        }
        // Build input for DynamoDB from user-controlled value
        input := &dynamodb.GetItemInput{
            TableName: aws.String("users"),
            Key: map[string]types.AttributeValue{
                "user_id": &types.AttributeValueMemberS{Value: userID},
            },
        }
        result, err := dynamoClient.GetItem(c.Request.Context(), input)
        if err != nil {
            c.AbortWithStatusJSON(500, gin.H{"error": "unable to fetch user"})
            return
        }
        // Store raw DynamoDB response in cache without normalization
        cache.Set(cacheKey, result.Item, time.Minute*5)
        c.JSON(200, result.Item)
    }
}

If userID is not validated, an attacker can supply values such as ..%2F..%2Fetc%2Fpasswd or crafted strings that change the effective cache key (e.g., user:..%2F..%2Fetc%2Fpasswd) and also alter the DynamoDB request’s key. This can lead to cache entries being shared across users, storing attacker-controlled or sensitive DynamoDB responses in shared cache slots, or causing cache evictions that degrade reliability. Moreover, because the cache key directly incorporates the user input, two logically distinct users may map to the same cache key, enabling one user to view another’s cached data.

Additionally, if the response from DynamoDB is cached without validating or normalizing sensitive fields (such as tokens or PII), the poisoned cache can expose sensitive information to other users. In this context, cache poisoning here is not only about HTTP response cache manipulation but also about the integrity of server-side cache stores that rely on attacker-influenced keys and DynamoDB query inputs.

To prevent cache poisoning when Gin interacts with DynamoDB, you must validate and sanitize all inputs used to construct cache keys and DynamoDB query parameters. Enforce strict allowlists for identifiers, avoid concatenating raw user input into cache keys, and normalize data before caching.

The following example demonstrates a safer pattern using input validation, deterministic cache keys, and safe DynamoDB parameter handling:

// Safe handler with validation and normalized cache key
import (
    "context"
    "net/http"
    "regexp"

    "github.com/gin-gonic/gin"
    "github.com/aws/aws-sdk-go-v2/aws"
    "github.com/aws/aws-sdk-go-v2/service/dynamodb"
    "github.com/aws/aws-sdk-go-v2/service/dynamodb/types"
)

var userIDRegex = regexp.MustCompile(`^[a-zA-Z0-9_-]{1,64}$`)

func getValidatedUserID(c *gin.Context) (string, bool) {
    userID := c.Param("userID")
    if !userIDRegex.MatchString(userID) {
        return "", false
    }
    return userID, true
}

func getUserSafeHandler(cache *lru.Cache) gin.HandlerFunc {
    return func(c *gin.Context) {
        userID, ok := getValidatedUserID(c)
        if !ok {
            c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "invalid user identifier"})
            return
        }
        // Deterministic cache key derived from a namespace and the validated ID
        cacheKey := "user:v1:" + userID
        if cached, ok := cache.Get(cacheKey); ok {
            c.JSON(http.StatusOK, cached)
            return
        }
        // Safe DynamoDB request using validated input
        input := &dynamodb.GetItemInput{
            TableName: aws.String("users"),
            Key: map[string]types.AttributeValue{
                "user_id": &types.AttributeValueMemberS{Value: userID},
            },
        }
        result, err := dynamoClient.GetItem(c.Request.Context(), input)
        if err != nil {
            c.AbortWithStatusJSON(http.StatusInternalServerError, gin.H{"error": "unable to fetch user"})
            return
        }
        // Ensure sensitive fields are handled before caching (example: remove credentials)
        sanitized := sanitizeDynamoDBItem(result.Item)
        cache.Set(cacheKey, sanitized, time.Minute*5)
        c.JSON(http.StatusOK, sanitized)
    }
}

// Example sanitization helper
func sanitizeDynamoDBItem(item map[string]types.AttributeValue) map[string]interface{} {
    out := make(map[string]interface{})
    for k, v := range item {
        // Example: exclude fields that should not be cached or exposed
        if k == "password_hash" || k == "api_key" {
            continue
        }
        if s := v.(*types.AttributeValueMemberS); s != nil {
            out[k] = s.Value
        } else if b := v.(*types.AttributeValueMemberBOOL); b != nil {
            out[k] = b.Value
        } else if n := v.(*types.AttributeValueMemberN); n != nil {
            out[k] = n.Value
        } else {
            out[k] = ""
        }
    }
    return out
}

Additional recommendations:

• Use middleware to enforce content normalization for all incoming request parameters that affect DynamoDB keys or queries.
• Apply rate limiting per validated user ID to reduce the impact of abusive cache-key variants.
• Ensure that cache entries are namespaced and versioned (e.g., v1:user:ID) to prevent cross-version or cross-user contamination.
• If you use the middleBrick CLI (middlebrick scan <url>) or GitHub Action, include these validation rules in your CI/CD pipeline to detect insecure patterns before deployment.